Overview of task

The assigned task was to download two datasets from the New York City Open Data website. One dataset contains information on the condomonidum rent income and the other has information on New York City community gardens. This script shows how I downloaded the datasets and processed them to answer the given questions.

Datasets

I downloaded the CSV versions of the datasets, although other versions like the RDF were available I did not see an added benefit in working with it for this task. Then I loaded the datasets into the R environment.

condoDataset <- read.csv(file = "/home/niru/allInfoDir/otherWorkspaces/geophy/DOF__Condominium_comparable_rental_income___Manhattan_-_FY_2010_2011.csv", as.is = T)
condoDataset <- condoDataset[!is.na(condoDataset$Latitude), ]
greenThumbDataset <- read.csv(file = "/home/niru/allInfoDir/otherWorkspaces/geophy/NYC_Greenthumb_Community_Gardens.csv", as.is = T)
greenThumbDataset <- greenThumbDataset[!is.na(greenThumbDataset$Latitude), ]

Task 1 - Visualize the datasets on a map

I used the R package ggmaps to plot the data points

library(ggmap)
## Loading required package: ggplot2

The Green Thumbs dataset has a bigger geographical spread than the condomonium dataset so I used that the center for the map of New York City.

centerGreen <- c(mean(range(greenThumbDataset$Longitude)), mean(range(greenThumbDataset$Latitude)))
mapNY <- get_googlemap(center = centerGreen, zoom = 11)
ggmap(mapNY)

Then I plotted the condomoniums (in black) and the community gardens (in purple).

mapPoints <- ggmap(mapNY) + 
  geom_point(aes(x = condoDataset$Longitude, y = condoDataset$Latitude), data = condoDataset, alpha = .5, col = "black") + 
  geom_point(aes(x = greenThumbDataset$Longitude, y = greenThumbDataset$Latitude), data = greenThumbDataset, alpha = 1, col = "purple") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank())
mapPoints

The two datasets overlapped in only a few areas. By overlaying the value per square foot I see that the overlapping areas have lower costs of condomoniums.

mapPointsValue <- ggmap(mapNY) + 
  geom_point(aes(x = condoDataset$Longitude, y = condoDataset$Latitude, color = condoDataset$MANHATTAN.CONDOMINIUM.PROPERTY.Market.Value.per.SqFt), data = condoDataset, alpha = .5) + 
  scale_color_gradient(high = "red4", low = "coral", name = "ValuePerSqFt") +
  geom_point(aes(x = greenThumbDataset$Longitude, y = greenThumbDataset$Latitude), data = greenThumbDataset, alpha = 1, col = "purple") +
  theme(axis.title.x = element_blank(), axis.title.y = element_blank())
mapPointsValue

Task 2 - Expensive Neighbourhood Tabulation Area

In order to look at the property values of the NTAs, I looked at the average of the properties in each NTA in the data.

condoDataset$NTA <- gsub("[[:blank:]]+", "", condoDataset$NTA)
allCondoNTA <- table(condoDataset$NTA)[order(table(condoDataset$NTA), decreasing = T)]
ntaCostDF <- data.frame(condoDataset$NTA, condoDataset$MANHATTAN.CONDOMINIUM.PROPERTY.Market.Value.per.SqFt, stringsAsFactors = F)
avgNTACostDF <- data.frame(NTA = names(allCondoNTA), AverageValue = numeric(length = length(allCondoNTA)), stringsAsFactors = F)

for (i in 1:length(allCondoNTA)) {
  tmpRows <- ntaCostDF[grep(names(allCondoNTA)[i], ntaCostDF$condoDataset.NTA, fixed = T), ]
  avgNTACostDF$AverageValue[i] <- mean(tmpRows[[2]])
}

There are a total of 28 NTAs in the condomonium dataset, of which the one called “park-cemetery-etc-Manhattan” had one property that had the highest value per square foot.

avgNTACostDF[which.max(avgNTACostDF$AverageValue), ]
##                            NTA AverageValue
## 28 park-cemetery-etc-Manhattan          299

Since this NTA only has one property listed there are not many conclusions that can be made, although a visualisation did show that it is right in the middle of Central Park.

priciestNTA <- avgNTACostDF$NTA[which.max(avgNTACostDF$AverageValue)]
priciestNTAData <- condoDataset[grep(priciestNTA, condoDataset$NTA, fixed = T), ]
mapPointsPriciest <- ggmap(mapNY) + 
  geom_point(aes(x = condoDataset$Longitude, y = condoDataset$Latitude), data = condoDataset, alpha = .5, col = "black") + 
  geom_point(aes(x = greenThumbDataset$Longitude, y = greenThumbDataset$Latitude), data = greenThumbDataset, alpha = .5, col = "purple") +
  geom_point(aes(x = priciestNTAData$Longitude, y = priciestNTAData$Latitude), data = priciestNTAData, alpha = .5, col = "red")
mapPointsPriciest

Task 3a - Green Thumb Data

There are 18 NTAs from the condominium dataset that have community gardens.

greenThumbDataset$NTA <- gsub("[[:blank:]]+", "",  greenThumbDataset$NTA)
commonNTA <- intersect(greenThumbDataset$NTA, names(allCondoNTA))
colours <- ifelse(avgNTACostDF$NTA %in% commonNTA, "purple", "black")
plot(avgNTACostDF$AverageValue, col = colours, xlab = "NTAs", ylab = "Value per square foot")

Based on the plots it seems to me that the properties in areas with community gardens have a less than average value per square foot for New York City.

Task 3b - Rental income

In order to complete this task I took as the rental income the values in the column “MANHATTAN.CONDOMINIUM.PROPERTY.Gross.Income.per.SqFt”. This column has values ranging from 0 to 67, I did find it odd that there were 2 zero values but based on the meta data I decided this was the best set of values to use. I compared the value and the rental income with a plot and a correlation

valueIncome <- data.frame(value = condoDataset$MANHATTAN.CONDOMINIUM.PROPERTY.Market.Value.per.SqFt, 
                          income = condoDataset$MANHATTAN.CONDOMINIUM.PROPERTY.Gross.Income.per.SqFt)
valueIncome <- valueIncome[order(valueIncome$value), ]
matplot((valueIncome), type = "l", lty = 1)

Except for very few points the two vectors follow the same trend accross properties.

cor(valueIncome$value, valueIncome$income)
## [1] 0.9753242

The extremely high correlation value also shows that the rental income could be an important determinant of property value.